Overview

Brought to you by YData

Dataset statistics

Number of variables47
Number of observations7691
Missing cells8036
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.2 MiB
Average record size in memory2.7 KiB

Variable types

Numeric4
DateTime1
Categorical39
Text2
Unsupported1

Alerts

Loss_code has constant value "LD003" Constant
Loss_description has constant value "Head on collision" Constant
TP_type_insd_pass_front has constant value " - " Constant
TP_type_pass_multi has constant value " - " Constant
Claim Number is highly overall correlated with Main_driver and 1 other fieldsHigh correlation
Notifier is highly overall correlated with Claim NumberHigh correlation
Location_of_incident is highly overall correlated with Weather_conditionsHigh correlation
Weather_conditions is highly overall correlated with Location_of_incidentHigh correlation
Vehicle_mobile is highly overall correlated with Time_hourHigh correlation
Time_hour is highly overall correlated with Vehicle_mobileHigh correlation
Main_driver is highly overall correlated with Claim Number and 1 other fieldsHigh correlation
PH_considered_TP_at_fault is highly overall correlated with Claim Number and 1 other fieldsHigh correlation
TP_type_driver is highly overall correlated with Claim Number and 4 other fieldsHigh correlation
TP_type_pass_back is highly overall correlated with TP_injury_whiplash and 2 other fieldsHigh correlation
TP_type_pass_front is highly overall correlated with TP_injury_whiplashHigh correlation
TP_type_other is highly overall correlated with TP_injury_unclear and 1 other fieldsHigh correlation
TP_type_nk is highly overall correlated with Claim Number and 5 other fieldsHigh correlation
TP_injury_whiplash is highly overall correlated with TP_type_pass_back and 5 other fieldsHigh correlation
TP_injury_traumatic is highly overall correlated with TP_region_northHigh correlation
TP_injury_fatality is highly overall correlated with TP_region_eastangHigh correlation
TP_injury_unclear is highly overall correlated with TP_type_driver and 5 other fieldsHigh correlation
TP_injury_nk is highly overall correlated with TP_type_nk and 1 other fieldsHigh correlation
TP_region_eastang is highly overall correlated with TP_injury_fatalityHigh correlation
TP_region_london is highly overall correlated with TP_type_pass_back and 1 other fieldsHigh correlation
TP_region_north is highly overall correlated with TP_injury_traumaticHigh correlation
TP_region_northw is highly overall correlated with TP_type_other and 1 other fieldsHigh correlation
TP_region_southw is highly overall correlated with TP_injury_unclearHigh correlation
TP_region_westmid is highly overall correlated with TP_type_pass_back and 1 other fieldsHigh correlation
Vechile_registration_present is highly imbalanced (99.1%) Imbalance
TP_type_insd_pass_back is highly imbalanced (92.7%) Imbalance
TP_type_driver is highly imbalanced (56.4%) Imbalance
TP_type_pass_back is highly imbalanced (91.9%) Imbalance
TP_type_pass_front is highly imbalanced (79.5%) Imbalance
TP_type_bike is highly imbalanced (96.1%) Imbalance
TP_type_cyclist is highly imbalanced (99.4%) Imbalance
TP_type_pedestrian is highly imbalanced (99.8%) Imbalance
TP_type_other is highly imbalanced (86.8%) Imbalance
TP_type_nk is highly imbalanced (60.4%) Imbalance
TP_injury_whiplash is highly imbalanced (69.3%) Imbalance
TP_injury_traumatic is highly imbalanced (83.7%) Imbalance
TP_injury_fatality is highly imbalanced (97.9%) Imbalance
TP_injury_unclear is highly imbalanced (63.8%) Imbalance
TP_injury_nk is highly imbalanced (54.3%) Imbalance
TP_region_eastang is highly imbalanced (91.1%) Imbalance
TP_region_eastmid is highly imbalanced (90.0%) Imbalance
TP_region_london is highly imbalanced (95.5%) Imbalance
TP_region_north is highly imbalanced (92.8%) Imbalance
TP_region_northw is highly imbalanced (88.8%) Imbalance
TP_region_outerldn is highly imbalanced (91.5%) Imbalance
TP_region_scotland is highly imbalanced (95.0%) Imbalance
TP_region_southe is highly imbalanced (83.4%) Imbalance
TP_region_southw is highly imbalanced (82.8%) Imbalance
TP_region_wales is highly imbalanced (89.3%) Imbalance
TP_region_westmid is highly imbalanced (86.9%) Imbalance
TP_region_yorkshire is highly imbalanced (84.9%) Imbalance
Weather_conditions has 345 (4.5%) missing values Missing
Unnamed: 46 has 7691 (100.0%) missing values Missing
Claim Number is uniformly distributed Uniform
Claim Number has unique values Unique
Unnamed: 46 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Notification_period has 3433 (44.6%) zeros Zeros
Time_hour has 357 (4.6%) zeros Zeros

Reproduction

Analysis started2025-07-04 14:03:33.200852
Analysis finished2025-07-04 14:03:40.843748
Duration7.64 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Claim Number
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct7691
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3846
Minimum1
Maximum7691
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-04T15:03:40.953870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile385.5
Q11923.5
median3846
Q35768.5
95-th percentile7306.5
Maximum7691
Range7690
Interquartile range (IQR)3845

Descriptive statistics

Standard deviation2220.3448
Coefficient of variation (CV)0.57731274
Kurtosis-1.2
Mean3846
Median Absolute Deviation (MAD)1923
Skewness0
Sum29579586
Variance4929931
MonotonicityStrictly increasing
2025-07-04T15:03:41.012454image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
5288 1
 
< 0.1%
5136 1
 
< 0.1%
5135 1
 
< 0.1%
5134 1
 
< 0.1%
5133 1
 
< 0.1%
5132 1
 
< 0.1%
5131 1
 
< 0.1%
5130 1
 
< 0.1%
5129 1
 
< 0.1%
Other values (7681) 7681
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
7691 1
< 0.1%
7690 1
< 0.1%
7689 1
< 0.1%
7688 1
< 0.1%
7687 1
< 0.1%
7686 1
< 0.1%
7685 1
< 0.1%
7684 1
< 0.1%
7683 1
< 0.1%
7682 1
< 0.1%
Distinct3175
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Memory size60.2 KiB
Minimum2003-04-15 00:00:00
Maximum2015-06-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-04T15:03:41.067158image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T15:03:41.122048image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Notifier
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size454.2 KiB
PH
3807 
Other
3117 
TP
 
325
CNF
 
262
NamedDriver
 
180

Length

Max length11
Median length2
Mean length3.4605383
Min length2

Characters and Unicode

Total characters26615
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPH
2nd rowCNF
3rd rowCNF
4th rowCNF
5th rowCNF

Common Values

ValueCountFrequency (%)
PH 3807
49.5%
Other 3117
40.5%
TP 325
 
4.2%
CNF 262
 
3.4%
NamedDriver 180
 
2.3%

Length

2025-07-04T15:03:41.171137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:41.214395image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ph 3807
49.5%
other 3117
40.5%
tp 325
 
4.2%
cnf 262
 
3.4%
nameddriver 180
 
2.3%

Most occurring characters

ValueCountFrequency (%)
P 4132
15.5%
H 3807
14.3%
e 3477
13.1%
r 3477
13.1%
O 3117
11.7%
t 3117
11.7%
h 3117
11.7%
N 442
 
1.7%
T 325
 
1.2%
F 262
 
1.0%
Other values (7) 1342
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 4132
15.5%
H 3807
14.3%
e 3477
13.1%
r 3477
13.1%
O 3117
11.7%
t 3117
11.7%
h 3117
11.7%
N 442
 
1.7%
T 325
 
1.2%
F 262
 
1.0%
Other values (7) 1342
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 4132
15.5%
H 3807
14.3%
e 3477
13.1%
r 3477
13.1%
O 3117
11.7%
t 3117
11.7%
h 3117
11.7%
N 442
 
1.7%
T 325
 
1.2%
F 262
 
1.0%
Other values (7) 1342
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 4132
15.5%
H 3807
14.3%
e 3477
13.1%
r 3477
13.1%
O 3117
11.7%
t 3117
11.7%
h 3117
11.7%
N 442
 
1.7%
T 325
 
1.2%
F 262
 
1.0%
Other values (7) 1342
 
5.0%

Loss_code
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size465.8 KiB
LD003
7691 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters38455
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLD003
2nd rowLD003
3rd rowLD003
4th rowLD003
5th rowLD003

Common Values

ValueCountFrequency (%)
LD003 7691
100.0%

Length

2025-07-04T15:03:41.254892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:41.290673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ld003 7691
100.0%

Most occurring characters

ValueCountFrequency (%)
0 15382
40.0%
L 7691
20.0%
D 7691
20.0%
3 7691
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38455
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15382
40.0%
L 7691
20.0%
D 7691
20.0%
3 7691
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38455
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15382
40.0%
L 7691
20.0%
D 7691
20.0%
3 7691
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38455
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15382
40.0%
L 7691
20.0%
D 7691
20.0%
3 7691
20.0%

Loss_description
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size555.9 KiB
Head on collision
7691 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters130747
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHead on collision
2nd rowHead on collision
3rd rowHead on collision
4th rowHead on collision
5th rowHead on collision

Common Values

ValueCountFrequency (%)
Head on collision 7691
100.0%

Length

2025-07-04T15:03:41.329563image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:41.366476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
head 7691
33.3%
on 7691
33.3%
collision 7691
33.3%

Most occurring characters

ValueCountFrequency (%)
o 23073
17.6%
15382
11.8%
n 15382
11.8%
l 15382
11.8%
i 15382
11.8%
H 7691
 
5.9%
e 7691
 
5.9%
a 7691
 
5.9%
d 7691
 
5.9%
c 7691
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130747
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 23073
17.6%
15382
11.8%
n 15382
11.8%
l 15382
11.8%
i 15382
11.8%
H 7691
 
5.9%
e 7691
 
5.9%
a 7691
 
5.9%
d 7691
 
5.9%
c 7691
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130747
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 23073
17.6%
15382
11.8%
n 15382
11.8%
l 15382
11.8%
i 15382
11.8%
H 7691
 
5.9%
e 7691
 
5.9%
a 7691
 
5.9%
d 7691
 
5.9%
c 7691
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130747
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 23073
17.6%
15382
11.8%
n 15382
11.8%
l 15382
11.8%
i 15382
11.8%
H 7691
 
5.9%
e 7691
 
5.9%
a 7691
 
5.9%
d 7691
 
5.9%
c 7691
 
5.9%

Notification_period
Real number (ℝ)

Zeros 

Distinct189
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1634378
Minimum-18
Maximum1042
Zeros3433
Zeros (%)44.6%
Negative3
Negative (%)< 0.1%
Memory size60.2 KiB
2025-07-04T15:03:41.415160image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-18
5-th percentile0
Q10
median1
Q32
95-th percentile24
Maximum1042
Range1060
Interquartile range (IQR)2

Descriptive statistics

Standard deviation39.138209
Coefficient of variation (CV)5.4636071
Kurtosis251.0534
Mean7.1634378
Median Absolute Deviation (MAD)1
Skewness13.596732
Sum55094
Variance1531.7994
MonotonicityNot monotonic
2025-07-04T15:03:41.477478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3433
44.6%
1 2046
26.6%
2 613
 
8.0%
3 303
 
3.9%
4 183
 
2.4%
5 122
 
1.6%
6 92
 
1.2%
7 75
 
1.0%
8 72
 
0.9%
10 57
 
0.7%
Other values (179) 695
 
9.0%
ValueCountFrequency (%)
-18 1
 
< 0.1%
-2 1
 
< 0.1%
-1 1
 
< 0.1%
0 3433
44.6%
1 2046
26.6%
2 613
 
8.0%
3 303
 
3.9%
4 183
 
2.4%
5 122
 
1.6%
6 92
 
1.2%
ValueCountFrequency (%)
1042 1
< 0.1%
961 1
< 0.1%
925 1
< 0.1%
856 1
< 0.1%
741 1
< 0.1%
720 1
< 0.1%
577 1
< 0.1%
546 1
< 0.1%
519 1
< 0.1%
510 1
< 0.1%

Inception_to_loss
Real number (ℝ)

Distinct366
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.85451
Minimum0
Maximum365
Zeros26
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-04T15:03:41.529062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q175
median161
Q3253
95-th percentile340
Maximum365
Range365
Interquartile range (IQR)178

Descriptive statistics

Standard deviation104.45291
Coefficient of variation (CV)0.6260119
Kurtosis-1.1557578
Mean166.85451
Median Absolute Deviation (MAD)89
Skewness0.17115136
Sum1283278
Variance10910.41
MonotonicityNot monotonic
2025-07-04T15:03:41.580099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 42
 
0.5%
91 36
 
0.5%
173 36
 
0.5%
67 36
 
0.5%
51 35
 
0.5%
66 34
 
0.4%
124 33
 
0.4%
102 33
 
0.4%
14 33
 
0.4%
53 33
 
0.4%
Other values (356) 7340
95.4%
ValueCountFrequency (%)
0 26
0.3%
1 32
0.4%
2 22
0.3%
3 27
0.4%
4 28
0.4%
5 27
0.4%
6 22
0.3%
7 30
0.4%
8 32
0.4%
9 24
0.3%
ValueCountFrequency (%)
365 8
 
0.1%
364 22
0.3%
363 17
0.2%
362 19
0.2%
361 12
0.2%
360 21
0.3%
359 9
0.1%
358 17
0.2%
357 20
0.3%
356 19
0.2%

Location_of_incident
Categorical

High correlation 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size498.6 KiB
Minor Road
4249 
Main Road
2702 
Car Park
 
225
n/k
 
213
Other
 
117
Other values (3)
 
185

Length

Max length14
Median length10
Mean length9.3699129
Min length3

Characters and Unicode

Total characters72064
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMain Road
2nd rowMain Road
3rd rowMain Road
4th rowMain Road
5th rowOther

Common Values

ValueCountFrequency (%)
Minor Road 4249
55.2%
Main Road 2702
35.1%
Car Park 225
 
2.9%
n/k 213
 
2.8%
Other 117
 
1.5%
Home Address 104
 
1.4%
Not Applicable 56
 
0.7%
Motorway 25
 
0.3%

Length

2025-07-04T15:03:41.625963image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:41.746670image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
road 6951
46.3%
minor 4249
28.3%
main 2702
 
18.0%
car 225
 
1.5%
park 225
 
1.5%
n/k 213
 
1.4%
other 117
 
0.8%
home 104
 
0.7%
address 104
 
0.7%
not 56
 
0.4%
Other values (2) 81
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 11410
15.8%
a 10184
14.1%
7336
10.2%
n 7164
9.9%
d 7159
9.9%
i 7007
9.7%
M 6976
9.7%
R 6951
9.6%
r 4945
6.9%
k 438
 
0.6%
Other values (18) 2494
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 11410
15.8%
a 10184
14.1%
7336
10.2%
n 7164
9.9%
d 7159
9.9%
i 7007
9.7%
M 6976
9.7%
R 6951
9.6%
r 4945
6.9%
k 438
 
0.6%
Other values (18) 2494
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 11410
15.8%
a 10184
14.1%
7336
10.2%
n 7164
9.9%
d 7159
9.9%
i 7007
9.7%
M 6976
9.7%
R 6951
9.6%
r 4945
6.9%
k 438
 
0.6%
Other values (18) 2494
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 11410
15.8%
a 10184
14.1%
7336
10.2%
n 7164
9.9%
d 7159
9.9%
i 7007
9.7%
M 6976
9.7%
R 6951
9.6%
r 4945
6.9%
k 438
 
0.6%
Other values (18) 2494
 
3.5%

Weather_conditions
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.1%
Missing345
Missing (%)4.5%
Memory size469.3 KiB
NORMAL
4564 
WET
1903 
N/K
 
450
SNOW,ICE,FOG
 
429

Length

Max length12
Median length6
Mean length5.3894637
Min length3

Characters and Unicode

Total characters39591
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORMAL
2nd rowWET
3rd rowWET
4th rowN/K
5th rowN/K

Common Values

ValueCountFrequency (%)
NORMAL 4564
59.3%
WET 1903
24.7%
N/K 450
 
5.9%
SNOW,ICE,FOG 429
 
5.6%
(Missing) 345
 
4.5%

Length

2025-07-04T15:03:41.793303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:41.830902image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
normal 4564
62.1%
wet 1903
25.9%
n/k 450
 
6.1%
snow,ice,fog 429
 
5.8%

Most occurring characters

ValueCountFrequency (%)
N 5443
13.7%
O 5422
13.7%
R 4564
11.5%
M 4564
11.5%
A 4564
11.5%
L 4564
11.5%
W 2332
5.9%
E 2332
5.9%
T 1903
 
4.8%
, 858
 
2.2%
Other values (7) 3045
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39591
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 5443
13.7%
O 5422
13.7%
R 4564
11.5%
M 4564
11.5%
A 4564
11.5%
L 4564
11.5%
W 2332
5.9%
E 2332
5.9%
T 1903
 
4.8%
, 858
 
2.2%
Other values (7) 3045
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39591
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 5443
13.7%
O 5422
13.7%
R 4564
11.5%
M 4564
11.5%
A 4564
11.5%
L 4564
11.5%
W 2332
5.9%
E 2332
5.9%
T 1903
 
4.8%
, 858
 
2.2%
Other values (7) 3045
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39591
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 5443
13.7%
O 5422
13.7%
R 4564
11.5%
M 4564
11.5%
A 4564
11.5%
L 4564
11.5%
W 2332
5.9%
E 2332
5.9%
T 1903
 
4.8%
, 858
 
2.2%
Other values (7) 3045
7.7%

Vehicle_mobile
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size436.6 KiB
Y
4046 
N
3203 
n/k
442 

Length

Max length3
Median length1
Mean length1.1149395
Min length1

Characters and Unicode

Total characters8575
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowN

Common Values

ValueCountFrequency (%)
Y 4046
52.6%
N 3203
41.6%
n/k 442
 
5.7%

Length

2025-07-04T15:03:41.877567image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:41.919343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
y 4046
52.6%
n 3203
41.6%
n/k 442
 
5.7%

Most occurring characters

ValueCountFrequency (%)
Y 4046
47.2%
N 3203
37.4%
n 442
 
5.2%
/ 442
 
5.2%
k 442
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8575
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 4046
47.2%
N 3203
37.4%
n 442
 
5.2%
/ 442
 
5.2%
k 442
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8575
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 4046
47.2%
N 3203
37.4%
n 442
 
5.2%
/ 442
 
5.2%
k 442
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8575
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 4046
47.2%
N 3203
37.4%
n 442
 
5.2%
/ 442
 
5.2%
k 442
 
5.2%

Time_hour
Real number (ℝ)

High correlation  Zeros 

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.729684
Minimum0
Maximum23
Zeros357
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-07-04T15:03:41.955618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median13
Q317
95-th percentile20
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.1071358
Coefficient of variation (CV)0.40119895
Kurtosis-0.051858402
Mean12.729684
Median Absolute Deviation (MAD)4
Skewness-0.4975176
Sum97904
Variance26.082837
MonotonicityNot monotonic
2025-07-04T15:03:41.995817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
8 747
 
9.7%
17 682
 
8.9%
15 612
 
8.0%
16 582
 
7.6%
18 556
 
7.2%
14 483
 
6.3%
13 481
 
6.3%
9 467
 
6.1%
12 449
 
5.8%
11 422
 
5.5%
Other values (14) 2210
28.7%
ValueCountFrequency (%)
0 357
4.6%
1 19
 
0.2%
2 14
 
0.2%
3 15
 
0.2%
4 12
 
0.2%
5 52
 
0.7%
6 121
 
1.6%
7 417
5.4%
8 747
9.7%
9 467
6.1%
ValueCountFrequency (%)
23 67
 
0.9%
22 109
 
1.4%
21 116
 
1.5%
20 205
 
2.7%
19 334
4.3%
18 556
7.2%
17 682
8.9%
16 582
7.6%
15 612
8.0%
14 483
6.3%

Main_driver
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size447.2 KiB
Y
4319 
Other
2940 
N
432 

Length

Max length5
Median length1
Mean length2.5290599
Min length1

Characters and Unicode

Total characters19451
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowOther
3rd rowY
4th rowOther
5th rowOther

Common Values

ValueCountFrequency (%)
Y 4319
56.2%
Other 2940
38.2%
N 432
 
5.6%

Length

2025-07-04T15:03:42.041181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:42.078888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
y 4319
56.2%
other 2940
38.2%
n 432
 
5.6%

Most occurring characters

ValueCountFrequency (%)
Y 4319
22.2%
O 2940
15.1%
t 2940
15.1%
h 2940
15.1%
e 2940
15.1%
r 2940
15.1%
N 432
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 4319
22.2%
O 2940
15.1%
t 2940
15.1%
h 2940
15.1%
e 2940
15.1%
r 2940
15.1%
N 432
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 4319
22.2%
O 2940
15.1%
t 2940
15.1%
h 2940
15.1%
e 2940
15.1%
r 2940
15.1%
N 432
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 4319
22.2%
O 2940
15.1%
t 2940
15.1%
h 2940
15.1%
e 2940
15.1%
r 2940
15.1%
N 432
 
2.2%

PH_considered_TP_at_fault
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size440.9 KiB
N
4855 
n/k
2654 
Y
 
181
#
 
1

Length

Max length3
Median length1
Mean length1.6901573
Min length1

Characters and Unicode

Total characters12999
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rown/k
2nd rown/k
3rd rown/k
4th rown/k
5th rown/k

Common Values

ValueCountFrequency (%)
N 4855
63.1%
n/k 2654
34.5%
Y 181
 
2.4%
# 1
 
< 0.1%

Length

2025-07-04T15:03:42.121242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:42.160913image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
n 4855
63.1%
n/k 2654
34.5%
y 181
 
2.4%
1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 4855
37.3%
n 2654
20.4%
/ 2654
20.4%
k 2654
20.4%
Y 181
 
1.4%
# 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12999
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 4855
37.3%
n 2654
20.4%
/ 2654
20.4%
k 2654
20.4%
Y 181
 
1.4%
# 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12999
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 4855
37.3%
n 2654
20.4%
/ 2654
20.4%
k 2654
20.4%
Y 181
 
1.4%
# 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12999
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 4855
37.3%
n 2654
20.4%
/ 2654
20.4%
k 2654
20.4%
Y 181
 
1.4%
# 1
 
< 0.1%

Vechile_registration_present
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.3 KiB
1
7685 
-
 
6

Length

Max length6
Median length4
Mean length4.0015603
Min length4

Characters and Unicode

Total characters30776
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 1
2nd row 1
3rd row 1
4th row 1
5th row 1

Common Values

ValueCountFrequency (%)
1 7685
99.9%
- 6
 
0.1%

Length

2025-07-04T15:03:42.207677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:42.249259image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 7685
99.9%
6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
15394
50.0%
7691
25.0%
1 7685
25.0%
- 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15394
50.0%
7691
25.0%
1 7685
25.0%
- 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15394
50.0%
7691
25.0%
1 7685
25.0%
- 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15394
50.0%
7691
25.0%
1 7685
25.0%
- 6
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size461.2 KiB
1
6217 
-
1474 

Length

Max length6
Median length4
Mean length4.3833052
Min length4

Characters and Unicode

Total characters33712
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row -
2nd row 1
3rd row -
4th row 1
5th row 1

Common Values

ValueCountFrequency (%)
1 6217
80.8%
- 1474
 
19.2%

Length

2025-07-04T15:03:42.292555image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:42.332799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 6217
80.8%
1474
 
19.2%

Most occurring characters

ValueCountFrequency (%)
18330
54.4%
7691
22.8%
1 6217
 
18.4%
- 1474
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18330
54.4%
7691
22.8%
1 6217
 
18.4%
- 1474
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18330
54.4%
7691
22.8%
1 6217
 
18.4%
- 1474
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18330
54.4%
7691
22.8%
1 6217
 
18.4%
- 1474
 
4.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size469.8 KiB
-
5906 
1
1785 

Length

Max length6
Median length6
Mean length5.5358211
Min length4

Characters and Unicode

Total characters42576
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 5906
76.8%
1 1785
 
23.2%

Length

2025-07-04T15:03:42.373336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:42.412635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
5906
76.8%
1 1785
 
23.2%

Most occurring characters

ValueCountFrequency (%)
27194
63.9%
7691
 
18.1%
- 5906
 
13.9%
1 1785
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
27194
63.9%
7691
 
18.1%
- 5906
 
13.9%
1 1785
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
27194
63.9%
7691
 
18.1%
- 5906
 
13.9%
1 1785
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
27194
63.9%
7691
 
18.1%
- 5906
 
13.9%
1 1785
 
4.2%

TP_type_insd_pass_back
Categorical

Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size473.0 KiB
-
7531 
1
 
110
2
 
38
3
 
11
4
 
1

Length

Max length6
Median length6
Mean length5.9583929
Min length4

Characters and Unicode

Total characters45826
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7531
97.9%
1 110
 
1.4%
2 38
 
0.5%
3 11
 
0.1%
4 1
 
< 0.1%

Length

2025-07-04T15:03:42.453759image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:42.494999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7531
97.9%
1 110
 
1.4%
2 38
 
0.5%
3 11
 
0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
30444
66.4%
7691
 
16.8%
- 7531
 
16.4%
1 110
 
0.2%
2 38
 
0.1%
3 11
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30444
66.4%
7691
 
16.8%
- 7531
 
16.4%
1 110
 
0.2%
2 38
 
0.1%
3 11
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30444
66.4%
7691
 
16.8%
- 7531
 
16.4%
1 110
 
0.2%
2 38
 
0.1%
3 11
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30444
66.4%
7691
 
16.8%
- 7531
 
16.4%
1 110
 
0.2%
2 38
 
0.1%
3 11
 
< 0.1%
4 1
 
< 0.1%

TP_type_insd_pass_front
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size473.3 KiB
-
7691 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters46146
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7691
100.0%

Length

2025-07-04T15:03:42.601606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:42.637108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7691
100.0%

Most occurring characters

ValueCountFrequency (%)
30764
66.7%
7691
 
16.7%
- 7691
 
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30764
66.7%
7691
 
16.7%
- 7691
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30764
66.7%
7691
 
16.7%
- 7691
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30764
66.7%
7691
 
16.7%
- 7691
 
16.7%

TP_type_driver
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size464.0 KiB
1
4588 
-
2907 
2
 
170
3
 
22
4
 
2

Length

Max length6
Median length4
Mean length4.7559485
Min length4

Characters and Unicode

Total characters36578
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
1 4588
59.7%
- 2907
37.8%
2 170
 
2.2%
3 22
 
0.3%
4 2
 
< 0.1%
5 2
 
< 0.1%

Length

2025-07-04T15:03:42.680470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:42.724039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 4588
59.7%
2907
37.8%
2 170
 
2.2%
3 22
 
0.3%
4 2
 
< 0.1%
5 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
21196
57.9%
7691
 
21.0%
1 4588
 
12.5%
- 2907
 
7.9%
2 170
 
0.5%
3 22
 
0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36578
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21196
57.9%
7691
 
21.0%
1 4588
 
12.5%
- 2907
 
7.9%
2 170
 
0.5%
3 22
 
0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36578
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21196
57.9%
7691
 
21.0%
1 4588
 
12.5%
- 2907
 
7.9%
2 170
 
0.5%
3 22
 
0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36578
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21196
57.9%
7691
 
21.0%
1 4588
 
12.5%
- 2907
 
7.9%
2 170
 
0.5%
3 22
 
0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%

TP_type_pass_back
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size472.9 KiB
-
7464 
1
 
160
2
 
48
3
 
15
5
 
2
Other values (2)
 
2

Length

Max length6
Median length6
Mean length5.94097
Min length4

Characters and Unicode

Total characters45692
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7464
97.0%
1 160
 
2.1%
2 48
 
0.6%
3 15
 
0.2%
5 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

Length

2025-07-04T15:03:42.770699image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:42.821609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7464
97.0%
1 160
 
2.1%
2 48
 
0.6%
3 15
 
0.2%
5 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
30310
66.3%
7691
 
16.8%
- 7464
 
16.3%
1 160
 
0.4%
2 48
 
0.1%
3 15
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45692
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30310
66.3%
7691
 
16.8%
- 7464
 
16.3%
1 160
 
0.4%
2 48
 
0.1%
3 15
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45692
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30310
66.3%
7691
 
16.8%
- 7464
 
16.3%
1 160
 
0.4%
2 48
 
0.1%
3 15
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45692
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30310
66.3%
7691
 
16.8%
- 7464
 
16.3%
1 160
 
0.4%
2 48
 
0.1%
3 15
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

TP_type_pass_front
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.5 KiB
-
7264 
1
 
405
2
 
22

Length

Max length6
Median length6
Mean length5.8889611
Min length4

Characters and Unicode

Total characters45292
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7264
94.4%
1 405
 
5.3%
2 22
 
0.3%

Length

2025-07-04T15:03:42.871776image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:42.913802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7264
94.4%
1 405
 
5.3%
2 22
 
0.3%

Most occurring characters

ValueCountFrequency (%)
29910
66.0%
7691
 
17.0%
- 7264
 
16.0%
1 405
 
0.9%
2 22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45292
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
29910
66.0%
7691
 
17.0%
- 7264
 
16.0%
1 405
 
0.9%
2 22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45292
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
29910
66.0%
7691
 
17.0%
- 7264
 
16.0%
1 405
 
0.9%
2 22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45292
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
29910
66.0%
7691
 
17.0%
- 7264
 
16.0%
1 405
 
0.9%
2 22
 
< 0.1%

TP_type_bike
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size473.2 KiB
-
7637 
1
 
52
2
 
2

Length

Max length6
Median length6
Mean length5.9859576
Min length4

Characters and Unicode

Total characters46038
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7637
99.3%
1 52
 
0.7%
2 2
 
< 0.1%

Length

2025-07-04T15:03:42.955424image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:42.997019image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7637
99.3%
1 52
 
0.7%
2 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
30656
66.6%
7691
 
16.7%
- 7637
 
16.6%
1 52
 
0.1%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46038
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30656
66.6%
7691
 
16.7%
- 7637
 
16.6%
1 52
 
0.1%
2 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46038
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30656
66.6%
7691
 
16.7%
- 7637
 
16.6%
1 52
 
0.1%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46038
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30656
66.6%
7691
 
16.7%
- 7637
 
16.6%
1 52
 
0.1%
2 2
 
< 0.1%

TP_type_cyclist
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size473.3 KiB
-
7687 
1
 
4

Length

Max length6
Median length6
Mean length5.9989598
Min length4

Characters and Unicode

Total characters46138
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7687
99.9%
1 4
 
0.1%

Length

2025-07-04T15:03:43.040126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:43.080355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7687
99.9%
1 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
30756
66.7%
7691
 
16.7%
- 7687
 
16.7%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30756
66.7%
7691
 
16.7%
- 7687
 
16.7%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30756
66.7%
7691
 
16.7%
- 7687
 
16.7%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30756
66.7%
7691
 
16.7%
- 7687
 
16.7%
1 4
 
< 0.1%

TP_type_pass_multi
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size473.3 KiB
-
7691 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters46146
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7691
100.0%

Length

2025-07-04T15:03:43.115022image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:43.146152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7691
100.0%

Most occurring characters

ValueCountFrequency (%)
30764
66.7%
7691
 
16.7%
- 7691
 
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30764
66.7%
7691
 
16.7%
- 7691
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30764
66.7%
7691
 
16.7%
- 7691
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30764
66.7%
7691
 
16.7%
- 7691
 
16.7%

TP_type_pedestrian
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size473.3 KiB
-
7690 
1
 
1

Length

Max length6
Median length6
Mean length5.99974
Min length4

Characters and Unicode

Total characters46144
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7690
> 99.9%
1 1
 
< 0.1%

Length

2025-07-04T15:03:43.184568image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:43.221399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7690
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
30762
66.7%
7691
 
16.7%
- 7690
 
16.7%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30762
66.7%
7691
 
16.7%
- 7690
 
16.7%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30762
66.7%
7691
 
16.7%
- 7690
 
16.7%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30762
66.7%
7691
 
16.7%
- 7690
 
16.7%
1 1
 
< 0.1%

TP_type_other
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size472.5 KiB
-
7257 
1
 
345
2
 
52
3
 
26
4
 
6
Other values (2)
 
5

Length

Max length6
Median length6
Mean length5.8871408
Min length4

Characters and Unicode

Total characters45278
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7257
94.4%
1 345
 
4.5%
2 52
 
0.7%
3 26
 
0.3%
4 6
 
0.1%
5 4
 
0.1%
6 1
 
< 0.1%

Length

2025-07-04T15:03:43.262598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:43.304786image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7257
94.4%
1 345
 
4.5%
2 52
 
0.7%
3 26
 
0.3%
4 6
 
0.1%
5 4
 
0.1%
6 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
29896
66.0%
7691
 
17.0%
- 7257
 
16.0%
1 345
 
0.8%
2 52
 
0.1%
3 26
 
0.1%
4 6
 
< 0.1%
5 4
 
< 0.1%
6 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
29896
66.0%
7691
 
17.0%
- 7257
 
16.0%
1 345
 
0.8%
2 52
 
0.1%
3 26
 
0.1%
4 6
 
< 0.1%
5 4
 
< 0.1%
6 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
29896
66.0%
7691
 
17.0%
- 7257
 
16.0%
1 345
 
0.8%
2 52
 
0.1%
3 26
 
0.1%
4 6
 
< 0.1%
5 4
 
< 0.1%
6 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
29896
66.0%
7691
 
17.0%
- 7257
 
16.0%
1 345
 
0.8%
2 52
 
0.1%
3 26
 
0.1%
4 6
 
< 0.1%
5 4
 
< 0.1%
6 1
 
< 0.1%

TP_type_nk
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size468.4 KiB
-
5159 
1
2394 
2
 
125
3
 
11
4
 
1

Length

Max length6
Median length6
Mean length5.3415681
Min length4

Characters and Unicode

Total characters41082
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row 1
2nd row 1
3rd row 1
4th row 1
5th row 1

Common Values

ValueCountFrequency (%)
- 5159
67.1%
1 2394
31.1%
2 125
 
1.6%
3 11
 
0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

Length

2025-07-04T15:03:43.352109image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:43.392495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
5159
67.1%
1 2394
31.1%
2 125
 
1.6%
3 11
 
0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
25700
62.6%
7691
 
18.7%
- 5159
 
12.6%
1 2394
 
5.8%
2 125
 
0.3%
3 11
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41082
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
25700
62.6%
7691
 
18.7%
- 5159
 
12.6%
1 2394
 
5.8%
2 125
 
0.3%
3 11
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41082
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
25700
62.6%
7691
 
18.7%
- 5159
 
12.6%
1 2394
 
5.8%
2 125
 
0.3%
3 11
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41082
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
25700
62.6%
7691
 
18.7%
- 5159
 
12.6%
1 2394
 
5.8%
2 125
 
0.3%
3 11
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

TP_injury_whiplash
Categorical

High correlation  Imbalance 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size470.5 KiB
-
6253 
1
1035 
2
 
283
3
 
82
4
 
24
Other values (3)
 
14

Length

Max length6
Median length6
Mean length5.6260564
Min length4

Characters and Unicode

Total characters43270
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 6253
81.3%
1 1035
 
13.5%
2 283
 
3.7%
3 82
 
1.1%
4 24
 
0.3%
5 10
 
0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%

Length

2025-07-04T15:03:43.439325image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:43.482564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
6253
81.3%
1 1035
 
13.5%
2 283
 
3.7%
3 82
 
1.1%
4 24
 
0.3%
5 10
 
0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
27888
64.5%
7691
 
17.8%
- 6253
 
14.5%
1 1035
 
2.4%
2 283
 
0.7%
3 82
 
0.2%
4 24
 
0.1%
5 10
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43270
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
27888
64.5%
7691
 
17.8%
- 6253
 
14.5%
1 1035
 
2.4%
2 283
 
0.7%
3 82
 
0.2%
4 24
 
0.1%
5 10
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43270
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
27888
64.5%
7691
 
17.8%
- 6253
 
14.5%
1 1035
 
2.4%
2 283
 
0.7%
3 82
 
0.2%
4 24
 
0.1%
5 10
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43270
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
27888
64.5%
7691
 
17.8%
- 6253
 
14.5%
1 1035
 
2.4%
2 283
 
0.7%
3 82
 
0.2%
4 24
 
0.1%
5 10
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%

TP_injury_traumatic
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size472.4 KiB
-
7214 
1
 
404
2
 
65
3
 
4
4
 
4

Length

Max length6
Median length6
Mean length5.8759589
Min length4

Characters and Unicode

Total characters45192
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7214
93.8%
1 404
 
5.3%
2 65
 
0.8%
3 4
 
0.1%
4 4
 
0.1%

Length

2025-07-04T15:03:43.532512image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:43.574735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7214
93.8%
1 404
 
5.3%
2 65
 
0.8%
3 4
 
0.1%
4 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
29810
66.0%
7691
 
17.0%
- 7214
 
16.0%
1 404
 
0.9%
2 65
 
0.1%
3 4
 
< 0.1%
4 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
29810
66.0%
7691
 
17.0%
- 7214
 
16.0%
1 404
 
0.9%
2 65
 
0.1%
3 4
 
< 0.1%
4 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
29810
66.0%
7691
 
17.0%
- 7214
 
16.0%
1 404
 
0.9%
2 65
 
0.1%
3 4
 
< 0.1%
4 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
29810
66.0%
7691
 
17.0%
- 7214
 
16.0%
1 404
 
0.9%
2 65
 
0.1%
3 4
 
< 0.1%
4 4
 
< 0.1%

TP_injury_fatality
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size473.3 KiB
-
7665 
1
 
24
2
 
2

Length

Max length6
Median length6
Mean length5.9932389
Min length4

Characters and Unicode

Total characters46094
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7665
99.7%
1 24
 
0.3%
2 2
 
< 0.1%

Length

2025-07-04T15:03:43.621619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:43.667181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7665
99.7%
1 24
 
0.3%
2 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
30712
66.6%
7691
 
16.7%
- 7665
 
16.6%
1 24
 
0.1%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46094
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30712
66.6%
7691
 
16.7%
- 7665
 
16.6%
1 24
 
0.1%
2 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46094
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30712
66.6%
7691
 
16.7%
- 7665
 
16.6%
1 24
 
0.1%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46094
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30712
66.6%
7691
 
16.7%
- 7665
 
16.6%
1 24
 
0.1%
2 2
 
< 0.1%

TP_injury_unclear
Categorical

High correlation  Imbalance 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size460.9 KiB
1
5797 
-
1342 
2
 
449
3
 
77
4
 
16
Other values (3)
 
10

Length

Max length6
Median length4
Mean length4.3489793
Min length4

Characters and Unicode

Total characters33448
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 1
2nd row 1
3rd row 1
4th row 1
5th row 1

Common Values

ValueCountFrequency (%)
1 5797
75.4%
- 1342
 
17.4%
2 449
 
5.8%
3 77
 
1.0%
4 16
 
0.2%
5 6
 
0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%

Length

2025-07-04T15:03:43.737517image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:43.783889image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 5797
75.4%
1342
 
17.4%
2 449
 
5.8%
3 77
 
1.0%
4 16
 
0.2%
5 6
 
0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
18066
54.0%
7691
23.0%
1 5797
 
17.3%
- 1342
 
4.0%
2 449
 
1.3%
3 77
 
0.2%
4 16
 
< 0.1%
5 6
 
< 0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18066
54.0%
7691
23.0%
1 5797
 
17.3%
- 1342
 
4.0%
2 449
 
1.3%
3 77
 
0.2%
4 16
 
< 0.1%
5 6
 
< 0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18066
54.0%
7691
23.0%
1 5797
 
17.3%
- 1342
 
4.0%
2 449
 
1.3%
3 77
 
0.2%
4 16
 
< 0.1%
5 6
 
< 0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18066
54.0%
7691
23.0%
1 5797
 
17.3%
- 1342
 
4.0%
2 449
 
1.3%
3 77
 
0.2%
4 16
 
< 0.1%
5 6
 
< 0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%

TP_injury_nk
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size465.3 KiB
1
3716 
-
3606 
2
 
284
3
 
51
4
 
25
Other values (2)
 
9

Length

Max length6
Median length4
Mean length4.9377194
Min length4

Characters and Unicode

Total characters37976
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row 1

Common Values

ValueCountFrequency (%)
1 3716
48.3%
- 3606
46.9%
2 284
 
3.7%
3 51
 
0.7%
4 25
 
0.3%
5 8
 
0.1%
6 1
 
< 0.1%

Length

2025-07-04T15:03:43.832971image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:43.874846image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 3716
48.3%
3606
46.9%
2 284
 
3.7%
3 51
 
0.7%
4 25
 
0.3%
5 8
 
0.1%
6 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
22594
59.5%
7691
 
20.3%
1 3716
 
9.8%
- 3606
 
9.5%
2 284
 
0.7%
3 51
 
0.1%
4 25
 
0.1%
5 8
 
< 0.1%
6 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22594
59.5%
7691
 
20.3%
1 3716
 
9.8%
- 3606
 
9.5%
2 284
 
0.7%
3 51
 
0.1%
4 25
 
0.1%
5 8
 
< 0.1%
6 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22594
59.5%
7691
 
20.3%
1 3716
 
9.8%
- 3606
 
9.5%
2 284
 
0.7%
3 51
 
0.1%
4 25
 
0.1%
5 8
 
< 0.1%
6 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22594
59.5%
7691
 
20.3%
1 3716
 
9.8%
- 3606
 
9.5%
2 284
 
0.7%
3 51
 
0.1%
4 25
 
0.1%
5 8
 
< 0.1%
6 1
 
< 0.1%

TP_region_eastang
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size472.8 KiB
-
7446 
1
 
204
2
 
27
3
 
12
4
 
1

Length

Max length6
Median length6
Mean length5.9362892
Min length4

Characters and Unicode

Total characters45656
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7446
96.8%
1 204
 
2.7%
2 27
 
0.4%
3 12
 
0.2%
4 1
 
< 0.1%
5 1
 
< 0.1%

Length

2025-07-04T15:03:44.011363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:44.053592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7446
96.8%
1 204
 
2.7%
2 27
 
0.4%
3 12
 
0.2%
4 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
30274
66.3%
7691
 
16.8%
- 7446
 
16.3%
1 204
 
0.4%
2 27
 
0.1%
3 12
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30274
66.3%
7691
 
16.8%
- 7446
 
16.3%
1 204
 
0.4%
2 27
 
0.1%
3 12
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30274
66.3%
7691
 
16.8%
- 7446
 
16.3%
1 204
 
0.4%
2 27
 
0.1%
3 12
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30274
66.3%
7691
 
16.8%
- 7446
 
16.3%
1 204
 
0.4%
2 27
 
0.1%
3 12
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%

TP_region_eastmid
Categorical

Imbalance 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size472.7 KiB
-
7382 
1
 
254
2
 
38
3
 
11
4
 
3
Other values (2)
 
3

Length

Max length6
Median length6
Mean length5.9196463
Min length4

Characters and Unicode

Total characters45528
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7382
96.0%
1 254
 
3.3%
2 38
 
0.5%
3 11
 
0.1%
4 3
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%

Length

2025-07-04T15:03:44.099781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:44.143182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7382
96.0%
1 254
 
3.3%
2 38
 
0.5%
3 11
 
0.1%
4 3
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
30146
66.2%
7691
 
16.9%
- 7382
 
16.2%
1 254
 
0.6%
2 38
 
0.1%
3 11
 
< 0.1%
4 3
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30146
66.2%
7691
 
16.9%
- 7382
 
16.2%
1 254
 
0.6%
2 38
 
0.1%
3 11
 
< 0.1%
4 3
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30146
66.2%
7691
 
16.9%
- 7382
 
16.2%
1 254
 
0.6%
2 38
 
0.1%
3 11
 
< 0.1%
4 3
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30146
66.2%
7691
 
16.9%
- 7382
 
16.2%
1 254
 
0.6%
2 38
 
0.1%
3 11
 
< 0.1%
4 3
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%

TP_region_london
Categorical

High correlation  Imbalance 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size473.1 KiB
-
7567 
1
 
104
2
 
12
4
 
3
3
 
2
Other values (3)
 
3

Length

Max length6
Median length6
Mean length5.9677545
Min length4

Characters and Unicode

Total characters45898
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7567
98.4%
1 104
 
1.4%
2 12
 
0.2%
4 3
 
< 0.1%
3 2
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%

Length

2025-07-04T15:03:44.191276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:44.235596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7567
98.4%
1 104
 
1.4%
2 12
 
0.2%
4 3
 
< 0.1%
3 2
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
30516
66.5%
7691
 
16.8%
- 7567
 
16.5%
1 104
 
0.2%
2 12
 
< 0.1%
4 3
 
< 0.1%
3 2
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45898
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30516
66.5%
7691
 
16.8%
- 7567
 
16.5%
1 104
 
0.2%
2 12
 
< 0.1%
4 3
 
< 0.1%
3 2
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45898
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30516
66.5%
7691
 
16.8%
- 7567
 
16.5%
1 104
 
0.2%
2 12
 
< 0.1%
4 3
 
< 0.1%
3 2
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45898
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30516
66.5%
7691
 
16.8%
- 7567
 
16.5%
1 104
 
0.2%
2 12
 
< 0.1%
4 3
 
< 0.1%
3 2
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%

TP_region_north
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size473.0 KiB
-
7528 
1
 
130
2
 
23
3
 
9
4
 
1

Length

Max length6
Median length6
Mean length5.9576128
Min length4

Characters and Unicode

Total characters45820
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7528
97.9%
1 130
 
1.7%
2 23
 
0.3%
3 9
 
0.1%
4 1
 
< 0.1%

Length

2025-07-04T15:03:44.283257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:44.324467image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7528
97.9%
1 130
 
1.7%
2 23
 
0.3%
3 9
 
0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
30438
66.4%
7691
 
16.8%
- 7528
 
16.4%
1 130
 
0.3%
2 23
 
0.1%
3 9
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30438
66.4%
7691
 
16.8%
- 7528
 
16.4%
1 130
 
0.3%
2 23
 
0.1%
3 9
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30438
66.4%
7691
 
16.8%
- 7528
 
16.4%
1 130
 
0.3%
2 23
 
0.1%
3 9
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30438
66.4%
7691
 
16.8%
- 7528
 
16.4%
1 130
 
0.3%
2 23
 
0.1%
3 9
 
< 0.1%
4 1
 
< 0.1%

TP_region_northw
Categorical

High correlation  Imbalance 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size472.6 KiB
-
7321 
1
 
276
2
 
64
3
 
18
4
 
8
Other values (3)
 
4

Length

Max length6
Median length6
Mean length5.9037836
Min length4

Characters and Unicode

Total characters45406
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row 1
2nd row -
3rd row 1
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7321
95.2%
1 276
 
3.6%
2 64
 
0.8%
3 18
 
0.2%
4 8
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%

Length

2025-07-04T15:03:44.368698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:44.412313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7321
95.2%
1 276
 
3.6%
2 64
 
0.8%
3 18
 
0.2%
4 8
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
30024
66.1%
7691
 
16.9%
- 7321
 
16.1%
1 276
 
0.6%
2 64
 
0.1%
3 18
 
< 0.1%
4 8
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30024
66.1%
7691
 
16.9%
- 7321
 
16.1%
1 276
 
0.6%
2 64
 
0.1%
3 18
 
< 0.1%
4 8
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30024
66.1%
7691
 
16.9%
- 7321
 
16.1%
1 276
 
0.6%
2 64
 
0.1%
3 18
 
< 0.1%
4 8
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30024
66.1%
7691
 
16.9%
- 7321
 
16.1%
1 276
 
0.6%
2 64
 
0.1%
3 18
 
< 0.1%
4 8
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%

TP_region_outerldn
Categorical

Imbalance 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size472.8 KiB
-
7457 
1
 
202
2
 
22
3
 
7
4
 
2

Length

Max length6
Median length6
Mean length5.9391497
Min length4

Characters and Unicode

Total characters45678
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7457
97.0%
1 202
 
2.6%
2 22
 
0.3%
3 7
 
0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%

Length

2025-07-04T15:03:44.460805image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:44.502826image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7457
97.0%
1 202
 
2.6%
2 22
 
0.3%
3 7
 
0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
30296
66.3%
7691
 
16.8%
- 7457
 
16.3%
1 202
 
0.4%
2 22
 
< 0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45678
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30296
66.3%
7691
 
16.8%
- 7457
 
16.3%
1 202
 
0.4%
2 22
 
< 0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45678
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30296
66.3%
7691
 
16.8%
- 7457
 
16.3%
1 202
 
0.4%
2 22
 
< 0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45678
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30296
66.3%
7691
 
16.8%
- 7457
 
16.3%
1 202
 
0.4%
2 22
 
< 0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%

TP_region_scotland
Categorical

Imbalance 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size473.1 KiB
-
7573 
1
 
95
2
 
19
5
 
2
4
 
1

Length

Max length6
Median length6
Mean length5.9693148
Min length4

Characters and Unicode

Total characters45910
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7573
98.5%
1 95
 
1.2%
2 19
 
0.2%
5 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

Length

2025-07-04T15:03:44.550016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:44.592292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7573
98.5%
1 95
 
1.2%
2 19
 
0.2%
5 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
30528
66.5%
7691
 
16.8%
- 7573
 
16.5%
1 95
 
0.2%
2 19
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45910
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30528
66.5%
7691
 
16.8%
- 7573
 
16.5%
1 95
 
0.2%
2 19
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45910
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30528
66.5%
7691
 
16.8%
- 7573
 
16.5%
1 95
 
0.2%
2 19
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45910
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30528
66.5%
7691
 
16.8%
- 7573
 
16.5%
1 95
 
0.2%
2 19
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%

TP_region_southe
Categorical

Imbalance 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size471.9 KiB
-
6969 
1
 
618
2
 
69
3
 
23
5
 
5
Other values (4)
 
7

Length

Max length6
Median length6
Mean length5.8122481
Min length4

Characters and Unicode

Total characters44702
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row -
2nd row 1
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 6969
90.6%
1 618
 
8.0%
2 69
 
0.9%
3 23
 
0.3%
5 5
 
0.1%
4 4
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%

Length

2025-07-04T15:03:44.637821image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:44.687701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
6969
90.6%
1 618
 
8.0%
2 69
 
0.9%
3 23
 
0.3%
5 5
 
0.1%
4 4
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
29320
65.6%
7691
 
17.2%
- 6969
 
15.6%
1 618
 
1.4%
2 69
 
0.2%
3 23
 
0.1%
5 5
 
< 0.1%
4 4
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
29320
65.6%
7691
 
17.2%
- 6969
 
15.6%
1 618
 
1.4%
2 69
 
0.2%
3 23
 
0.1%
5 5
 
< 0.1%
4 4
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
29320
65.6%
7691
 
17.2%
- 6969
 
15.6%
1 618
 
1.4%
2 69
 
0.2%
3 23
 
0.1%
5 5
 
< 0.1%
4 4
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
29320
65.6%
7691
 
17.2%
- 6969
 
15.6%
1 618
 
1.4%
2 69
 
0.2%
3 23
 
0.1%
5 5
 
< 0.1%
4 4
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%

TP_region_southw
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size472.1 KiB
-
7091 
1
 
530
2
 
53
3
 
11
4
 
5

Length

Max length6
Median length6
Mean length5.8439735
Min length4

Characters and Unicode

Total characters44946
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7091
92.2%
1 530
 
6.9%
2 53
 
0.7%
3 11
 
0.1%
4 5
 
0.1%
5 1
 
< 0.1%

Length

2025-07-04T15:03:44.749400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:44.803915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7091
92.2%
1 530
 
6.9%
2 53
 
0.7%
3 11
 
0.1%
4 5
 
0.1%
5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
29564
65.8%
7691
 
17.1%
- 7091
 
15.8%
1 530
 
1.2%
2 53
 
0.1%
3 11
 
< 0.1%
4 5
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44946
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
29564
65.8%
7691
 
17.1%
- 7091
 
15.8%
1 530
 
1.2%
2 53
 
0.1%
3 11
 
< 0.1%
4 5
 
< 0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44946
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
29564
65.8%
7691
 
17.1%
- 7091
 
15.8%
1 530
 
1.2%
2 53
 
0.1%
3 11
 
< 0.1%
4 5
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44946
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
29564
65.8%
7691
 
17.1%
- 7091
 
15.8%
1 530
 
1.2%
2 53
 
0.1%
3 11
 
< 0.1%
4 5
 
< 0.1%
5 1
 
< 0.1%

TP_region_wales
Categorical

Imbalance 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size472.7 KiB
-
7357 
1
 
274
2
 
36
3
 
14
4
 
5
Other values (2)
 
5

Length

Max length6
Median length6
Mean length5.9131452
Min length4

Characters and Unicode

Total characters45478
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7357
95.7%
1 274
 
3.6%
2 36
 
0.5%
3 14
 
0.2%
4 5
 
0.1%
5 4
 
0.1%
7 1
 
< 0.1%

Length

2025-07-04T15:03:44.858443image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:44.910777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7357
95.7%
1 274
 
3.6%
2 36
 
0.5%
3 14
 
0.2%
4 5
 
0.1%
5 4
 
0.1%
7 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
30096
66.2%
7691
 
16.9%
- 7357
 
16.2%
1 274
 
0.6%
2 36
 
0.1%
3 14
 
< 0.1%
4 5
 
< 0.1%
5 4
 
< 0.1%
7 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45478
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30096
66.2%
7691
 
16.9%
- 7357
 
16.2%
1 274
 
0.6%
2 36
 
0.1%
3 14
 
< 0.1%
4 5
 
< 0.1%
5 4
 
< 0.1%
7 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45478
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30096
66.2%
7691
 
16.9%
- 7357
 
16.2%
1 274
 
0.6%
2 36
 
0.1%
3 14
 
< 0.1%
4 5
 
< 0.1%
5 4
 
< 0.1%
7 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45478
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30096
66.2%
7691
 
16.9%
- 7357
 
16.2%
1 274
 
0.6%
2 36
 
0.1%
3 14
 
< 0.1%
4 5
 
< 0.1%
5 4
 
< 0.1%
7 1
 
< 0.1%

TP_region_westmid
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size472.5 KiB
-
7262 
1
 
341
2
 
54
3
 
21
4
 
9
Other values (2)
 
4

Length

Max length6
Median length6
Mean length5.888441
Min length4

Characters and Unicode

Total characters45288
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row -
2nd row -
3rd row -
4th row 1
5th row -

Common Values

ValueCountFrequency (%)
- 7262
94.4%
1 341
 
4.4%
2 54
 
0.7%
3 21
 
0.3%
4 9
 
0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%

Length

2025-07-04T15:03:44.964287image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:45.008818image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7262
94.4%
1 341
 
4.4%
2 54
 
0.7%
3 21
 
0.3%
4 9
 
0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
29906
66.0%
7691
 
17.0%
- 7262
 
16.0%
1 341
 
0.8%
2 54
 
0.1%
3 21
 
< 0.1%
4 9
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
29906
66.0%
7691
 
17.0%
- 7262
 
16.0%
1 341
 
0.8%
2 54
 
0.1%
3 21
 
< 0.1%
4 9
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
29906
66.0%
7691
 
17.0%
- 7262
 
16.0%
1 341
 
0.8%
2 54
 
0.1%
3 21
 
< 0.1%
4 9
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
29906
66.0%
7691
 
17.0%
- 7262
 
16.0%
1 341
 
0.8%
2 54
 
0.1%
3 21
 
< 0.1%
4 9
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%

TP_region_yorkshire
Categorical

Imbalance 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size472.4 KiB
-
7231 
1
 
362
2
 
64
3
 
19
4
 
11

Length

Max length6
Median length6
Mean length5.8803797
Min length4

Characters and Unicode

Total characters45226
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row -

Common Values

ValueCountFrequency (%)
- 7231
94.0%
1 362
 
4.7%
2 64
 
0.8%
3 19
 
0.2%
4 11
 
0.1%
5 4
 
0.1%

Length

2025-07-04T15:03:45.057321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T15:03:45.099544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7231
94.0%
1 362
 
4.7%
2 64
 
0.8%
3 19
 
0.2%
4 11
 
0.1%
5 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
29844
66.0%
7691
 
17.0%
- 7231
 
16.0%
1 362
 
0.8%
2 64
 
0.1%
3 19
 
< 0.1%
4 11
 
< 0.1%
5 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45226
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
29844
66.0%
7691
 
17.0%
- 7231
 
16.0%
1 362
 
0.8%
2 64
 
0.1%
3 19
 
< 0.1%
4 11
 
< 0.1%
5 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45226
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
29844
66.0%
7691
 
17.0%
- 7231
 
16.0%
1 362
 
0.8%
2 64
 
0.1%
3 19
 
< 0.1%
4 11
 
< 0.1%
5 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45226
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
29844
66.0%
7691
 
17.0%
- 7231
 
16.0%
1 362
 
0.8%
2 64
 
0.1%
3 19
 
< 0.1%
4 11
 
< 0.1%
5 4
 
< 0.1%
Distinct4327
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Memory size746.5 KiB
2025-07-04T15:03:45.221686image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length11
Mean length8.1898323
Min length5

Characters and Unicode

Total characters62988
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3471 ?
Unique (%)45.1%

Sample

1st row £ -
2nd row £ 2,801
3rd row £ 1,221
4th row £ 3,530
5th row £ 3,156
ValueCountFrequency (%)
£ 7691
50.0%
1754
 
11.4%
23 55
 
0.4%
24 28
 
0.2%
86 23
 
0.1%
19 18
 
0.1%
78 18
 
0.1%
84 18
 
0.1%
85 18
 
0.1%
70 16
 
0.1%
Other values (4287) 5743
37.3%
2025-07-04T15:03:45.395480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18842
29.9%
£ 7691
12.2%
7691
12.2%
, 4069
 
6.5%
1 3626
 
5.8%
2 2917
 
4.6%
3 2404
 
3.8%
4 2257
 
3.6%
5 2067
 
3.3%
7 2038
 
3.2%
Other values (5) 9386
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62988
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18842
29.9%
£ 7691
12.2%
7691
12.2%
, 4069
 
6.5%
1 3626
 
5.8%
2 2917
 
4.6%
3 2404
 
3.8%
4 2257
 
3.6%
5 2067
 
3.3%
7 2038
 
3.2%
Other values (5) 9386
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62988
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18842
29.9%
£ 7691
12.2%
7691
12.2%
, 4069
 
6.5%
1 3626
 
5.8%
2 2917
 
4.6%
3 2404
 
3.8%
4 2257
 
3.6%
5 2067
 
3.3%
7 2038
 
3.2%
Other values (5) 9386
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62988
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18842
29.9%
£ 7691
12.2%
7691
12.2%
, 4069
 
6.5%
1 3626
 
5.8%
2 2917
 
4.6%
3 2404
 
3.8%
4 2257
 
3.6%
5 2067
 
3.3%
7 2038
 
3.2%
Other values (5) 9386
14.9%
Distinct4130
Distinct (%)53.7%
Missing0
Missing (%)0.0%
Memory size746.4 KiB
2025-07-04T15:03:45.512000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.1790404
Min length5

Characters and Unicode

Total characters62905
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3273 ?
Unique (%)42.6%

Sample

1st row £ -
2nd row £ 2,801
3rd row £ 1,221
4th row £ 3,530
5th row £ 3,156
ValueCountFrequency (%)
£ 7691
50.0%
1754
 
11.4%
50,000 198
 
1.3%
23 55
 
0.4%
24 28
 
0.2%
86 23
 
0.1%
84 18
 
0.1%
19 18
 
0.1%
85 18
 
0.1%
78 18
 
0.1%
Other values (4090) 5561
36.2%
2025-07-04T15:03:45.665616image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18842
30.0%
£ 7691
12.2%
7691
12.2%
, 4066
 
6.5%
1 3490
 
5.5%
2 2813
 
4.5%
0 2501
 
4.0%
3 2306
 
3.7%
4 2178
 
3.5%
5 2135
 
3.4%
Other values (5) 9192
14.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62905
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18842
30.0%
£ 7691
12.2%
7691
12.2%
, 4066
 
6.5%
1 3490
 
5.5%
2 2813
 
4.5%
0 2501
 
4.0%
3 2306
 
3.7%
4 2178
 
3.5%
5 2135
 
3.4%
Other values (5) 9192
14.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62905
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18842
30.0%
£ 7691
12.2%
7691
12.2%
, 4066
 
6.5%
1 3490
 
5.5%
2 2813
 
4.5%
0 2501
 
4.0%
3 2306
 
3.7%
4 2178
 
3.5%
5 2135
 
3.4%
Other values (5) 9192
14.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62905
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18842
30.0%
£ 7691
12.2%
7691
12.2%
, 4066
 
6.5%
1 3490
 
5.5%
2 2813
 
4.5%
0 2501
 
4.0%
3 2306
 
3.7%
4 2178
 
3.5%
5 2135
 
3.4%
Other values (5) 9192
14.6%

Unnamed: 46
Unsupported

Missing  Rejected  Unsupported 

Missing7691
Missing (%)100.0%
Memory size60.2 KiB

Interactions

2025-07-04T15:03:40.298606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T15:03:39.637327image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T15:03:40.003760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T15:03:40.149248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T15:03:40.336815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T15:03:39.709002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T15:03:40.043483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T15:03:40.189781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T15:03:40.370008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T15:03:39.781121image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T15:03:40.078326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T15:03:40.225621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T15:03:40.406360image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T15:03:39.875075image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T15:03:40.115824image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-04T15:03:40.264085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-07-04T15:03:45.701121image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Claim NumberNotification_periodInception_to_lossTime_hourUnnamed: 46
Claim Number1.0000.0260.028-0.001NaN
Notification_period0.0261.000-0.021-0.156NaN
Inception_to_loss0.028-0.0211.000-0.007NaN
Time_hour-0.001-0.156-0.0071.000NaN
Unnamed: 46NaNNaNNaNNaNNaN
2025-07-04T15:03:45.746024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Claim NumberNotification_periodInception_to_lossTime_hourUnnamed: 46
Claim Number1.000-0.0580.029-0.003NaN
Notification_period-0.0581.000-0.0110.184NaN
Inception_to_loss0.029-0.0111.000-0.008NaN
Time_hour-0.0030.184-0.0081.000NaN
Unnamed: 46NaNNaNNaNNaNNaN
2025-07-04T15:03:45.791801image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Claim NumberNotification_periodInception_to_lossTime_hourUnnamed: 46
Claim Number1.000-0.0430.019-0.002NaN
Notification_period-0.0431.000-0.0080.147NaN
Inception_to_loss0.019-0.0081.000-0.006NaN
Time_hour-0.0020.147-0.0061.000NaN
Unnamed: 46NaNNaNNaNNaNNaN
2025-07-04T15:03:45.861893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Claim NumberNotifierNotification_periodInception_to_lossLocation_of_incidentWeather_conditionsVehicle_mobileTime_hourMain_driverPH_considered_TP_at_faultVechile_registration_presentIncident_details_presentInjury_details_presentTP_type_insd_pass_backTP_type_driverTP_type_pass_backTP_type_pass_frontTP_type_bikeTP_type_cyclistTP_type_pedestrianTP_type_otherTP_type_nkTP_injury_whiplashTP_injury_traumaticTP_injury_fatalityTP_injury_unclearTP_injury_nkTP_region_eastangTP_region_eastmidTP_region_londonTP_region_northTP_region_northwTP_region_outerldnTP_region_scotlandTP_region_southeTP_region_southwTP_region_walesTP_region_westmidTP_region_yorkshire
Claim Number1.0000.7460.0510.0000.1160.2420.2020.0580.6910.7460.0190.3910.0850.0960.5600.0380.0740.0000.0000.0010.0900.5460.0940.1350.0450.1050.1410.0000.0000.0200.0230.0000.0000.0000.0890.0850.0340.0000.039
Notifier0.7461.0000.2360.0000.2490.2900.3760.3270.2680.1540.0130.0980.0760.0000.1330.0660.0420.0000.0000.0000.0470.1130.0510.0240.0000.0670.0600.0110.0640.0000.0000.0340.0000.0000.0650.0260.0540.0520.030
Notification_period0.0510.2361.0000.0000.1900.2210.3440.2500.0970.0210.0000.0590.0670.0000.0000.0000.0000.0580.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0970.0000.0000.0000.0000.0000.0000.0000.0000.000
Inception_to_loss0.0000.0000.0001.0000.0000.0060.0000.0310.0000.0000.0000.0260.0000.0000.0000.0100.0000.0000.0000.0090.0000.0000.0190.0570.0000.0000.0000.0000.0000.0060.0000.0000.0230.0000.0110.0000.0000.0350.000
Location_of_incident0.1160.2490.1900.0001.0000.5180.4780.3080.1950.0960.0240.0660.2280.0350.0770.0220.0720.0000.0480.0000.0730.0510.0940.0670.0480.1480.0540.0000.0500.1100.0000.1250.0190.0000.1520.0670.0690.0000.023
Weather_conditions0.2420.2900.2210.0060.5181.0000.4060.4520.1370.0530.0500.0910.1200.0000.0560.0580.0190.0000.0000.0000.0590.0230.0510.0000.0470.0870.0330.0400.0000.0220.0000.0000.0000.0510.0630.0480.0440.0000.017
Vehicle_mobile0.2020.3760.3440.0000.4780.4061.0000.5290.4600.0980.0040.0420.2290.0810.2050.1080.2250.0000.0000.0000.1170.1640.2000.1400.1280.2280.1390.0960.0540.0840.0520.0290.0000.0790.1410.0760.0890.0460.088
Time_hour0.0580.3270.2500.0310.3080.4520.5291.0000.1990.0000.0000.0500.1780.0730.0440.0270.0880.0000.0000.0000.0630.0230.0730.1400.0310.0830.0520.0370.0400.1570.1100.0290.0420.0000.0610.0480.0290.0340.000
Main_driver0.6910.2680.0970.0000.1950.1370.4600.1991.0000.5100.0120.1020.0160.0290.7150.0000.0670.0660.0070.0000.0860.6930.0410.0750.0000.0800.1550.0530.0000.0190.0400.0000.0270.0000.1080.1510.0210.0520.051
PH_considered_TP_at_fault0.7460.1540.0210.0000.0960.0530.0980.0000.5101.0000.0000.3200.0290.0360.5690.0000.0400.0000.0000.0000.0660.5470.1050.0790.0000.1200.1240.0000.0000.0000.0000.0000.0000.0000.0610.0750.0020.0000.026
Vechile_registration_present0.0190.0130.0000.0000.0240.0500.0040.0000.0120.0001.0000.0000.0000.0000.1170.0060.0000.0000.0000.0000.1530.0000.1030.0000.0000.3870.0450.0000.0000.0000.0000.0000.0000.0000.0350.0000.3810.0000.000
Incident_details_present0.3910.0980.0590.0260.0660.0910.0420.0500.1020.3200.0001.0000.1520.0220.3430.0000.0000.0000.0000.0000.0150.3380.0390.0160.0010.0180.0440.0000.0180.0050.0060.0250.0000.0000.0280.0000.0000.0130.006
Injury_details_present0.0850.0760.0670.0000.2280.1200.2290.1780.0160.0290.0000.1521.0000.1170.1210.0260.0520.0000.0000.0000.0570.0840.1780.1540.0230.1980.1340.0700.0550.0000.0370.0570.0510.0860.0360.0490.0290.0200.078
TP_type_insd_pass_back0.0960.0000.0000.0000.0350.0000.0810.0730.0290.0360.0000.0220.1171.0000.0380.0490.0520.0000.0000.0000.0000.0470.2400.3600.1670.3800.3300.1330.0640.3260.1160.2240.0000.0800.3240.0270.1460.0450.142
TP_type_driver0.5600.1330.0000.0000.0770.0560.2050.0440.7150.5690.1170.3430.1210.0381.0000.0920.2760.1500.0000.0990.0820.7680.2530.1540.0780.5500.3410.0660.0640.0000.0360.0810.1280.0000.2190.0920.0540.0780.114
TP_type_pass_back0.0380.0660.0000.0100.0220.0580.1080.0270.0000.0000.0060.0000.0260.0490.0921.0000.3530.0000.0000.0000.1280.0690.7170.1980.0000.1600.3180.0940.4460.6470.1450.1680.2360.1830.4370.1840.3360.7170.221
TP_type_pass_front0.0740.0420.0000.0000.0720.0190.2250.0880.0670.0400.0000.0000.0520.0520.2760.3531.0000.0000.0070.0000.1030.2220.5800.2950.1730.2800.1630.2710.2010.1000.1030.3250.3010.2100.3290.2940.2840.2450.367
TP_type_bike0.0000.0000.0580.0000.0000.0000.0000.0000.0660.0000.0000.0000.0000.0000.1500.0000.0001.0000.0000.0000.0000.0800.0830.0430.0170.0780.0150.0000.0000.0000.0000.0870.0000.0000.0000.0000.0000.0930.000
TP_type_cyclist0.0000.0000.0490.0000.0480.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0070.0001.0000.0000.0000.0000.0290.0000.0000.0230.0000.0000.1380.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
TP_type_pedestrian0.0010.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0990.0000.0000.0000.0001.0000.0000.0000.0000.0990.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2020.000
TP_type_other0.0900.0470.0000.0000.0730.0590.1170.0630.0860.0660.1530.0150.0570.0000.0820.1280.1030.0000.0000.0001.0000.1140.2320.1050.1260.5760.3410.0550.1760.2160.0000.6440.1030.0710.0950.3950.4570.3650.100
TP_type_nk0.5460.1130.0000.0000.0510.0230.1640.0230.6930.5470.0000.3380.0840.0470.7680.0690.2220.0800.0000.0000.1141.0000.2320.1080.0020.6810.6450.0600.0220.0000.0000.1880.0400.0000.1140.1550.0170.0710.141
TP_injury_whiplash0.0940.0510.0000.0190.0940.0510.2000.0730.0410.1050.1030.0390.1780.2400.2530.7170.5800.0830.0290.0000.2320.2321.0000.1590.0210.5900.2880.0950.2340.6610.1780.5510.2210.2750.2990.1610.2710.5550.322
TP_injury_traumatic0.1350.0240.0000.0570.0670.0000.1400.1400.0750.0790.0000.0160.1540.3600.1540.1980.2950.0430.0000.0990.1050.1080.1591.0000.1220.2520.1380.1110.1830.0370.5830.2980.0690.0950.4600.0860.1130.1030.151
TP_injury_fatality0.0450.0000.0000.0000.0480.0470.1280.0310.0000.0000.0000.0010.0230.1670.0780.0000.1730.0170.0000.0000.1260.0020.0210.1221.0000.0440.1020.8330.0000.0000.0000.1550.0000.0000.4480.0860.0000.0430.050
TP_injury_unclear0.1050.0670.0000.0000.1480.0870.2280.0830.0800.1200.3870.0180.1980.3800.5500.1600.2800.0780.0230.0460.5760.6810.5900.2520.0441.0000.6500.0720.1050.3050.0620.4710.0620.0440.1360.5230.4950.1060.111
TP_injury_nk0.1410.0600.0000.0000.0540.0330.1390.0520.1550.1240.0450.0440.1340.3300.3410.3180.1630.0150.0000.0000.3410.6450.2880.1380.1020.6501.0000.0950.1720.0840.0730.1110.0910.0650.2090.1950.2300.2320.142
TP_region_eastang0.0000.0110.0000.0000.0000.0400.0960.0370.0530.0000.0000.0000.0700.1330.0660.0940.2710.0000.0000.0000.0550.0600.0950.1110.8330.0720.0951.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
TP_region_eastmid0.0000.0640.0000.0000.0500.0000.0540.0400.0000.0000.0000.0180.0550.0640.0640.4460.2010.0000.1380.0000.1760.0220.2340.1830.0000.1050.1720.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
TP_region_london0.0200.0000.0970.0060.1100.0220.0840.1570.0190.0000.0000.0050.0000.3260.0000.6470.1000.0000.0000.0000.2160.0000.6610.0370.0000.3050.0840.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
TP_region_north0.0230.0000.0000.0000.0000.0000.0520.1100.0400.0000.0000.0060.0370.1160.0360.1450.1030.0000.0000.0000.0000.0000.1780.5830.0000.0620.0730.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
TP_region_northw0.0000.0340.0000.0000.1250.0000.0290.0290.0000.0000.0000.0250.0570.2240.0810.1680.3250.0870.0000.0000.6440.1880.5510.2980.1550.4710.1110.0000.0000.0000.0001.0000.0350.0000.0000.0000.0000.0000.006
TP_region_outerldn0.0000.0000.0000.0230.0190.0000.0000.0420.0270.0000.0000.0000.0510.0000.1280.2360.3010.0000.0000.0000.1030.0400.2210.0690.0000.0620.0910.0000.0000.0000.0000.0351.0000.0000.0000.0000.0000.0000.000
TP_region_scotland0.0000.0000.0000.0000.0000.0510.0790.0000.0000.0000.0000.0000.0860.0800.0000.1830.2100.0000.0000.0000.0710.0000.2750.0950.0000.0440.0650.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
TP_region_southe0.0890.0650.0000.0110.1520.0630.1410.0610.1080.0610.0350.0280.0360.3240.2190.4370.3290.0000.0000.0000.0950.1140.2990.4600.4480.1360.2090.0000.0000.0000.0000.0000.0000.0001.0000.0470.0000.0050.027
TP_region_southw0.0850.0260.0000.0000.0670.0480.0760.0480.1510.0750.0000.0000.0490.0270.0920.1840.2940.0000.0000.0000.3950.1550.1610.0860.0860.5230.1950.0000.0000.0000.0000.0000.0000.0000.0471.0000.0000.0460.029
TP_region_wales0.0340.0540.0000.0000.0690.0440.0890.0290.0210.0020.3810.0000.0290.1460.0540.3360.2840.0000.0000.0000.4570.0170.2710.1130.0000.4950.2300.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
TP_region_westmid0.0000.0520.0000.0350.0000.0000.0460.0340.0520.0000.0000.0130.0200.0450.0780.7170.2450.0930.0000.2020.3650.0710.5550.1030.0430.1060.2320.0000.0000.0000.0000.0000.0000.0000.0050.0460.0001.0000.000
TP_region_yorkshire0.0390.0300.0000.0000.0230.0170.0880.0000.0510.0260.0000.0060.0780.1420.1140.2210.3670.0000.0000.0000.1000.1410.3220.1510.0500.1110.1420.0000.0000.0000.0000.0060.0000.0000.0270.0290.0000.0001.000
2025-07-04T15:03:46.073833image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Incident_details_presentInjury_details_presentLocation_of_incidentMain_driverNotifierPH_considered_TP_at_faultTP_injury_fatalityTP_injury_nkTP_injury_traumaticTP_injury_unclearTP_injury_whiplashTP_region_eastangTP_region_eastmidTP_region_londonTP_region_northTP_region_northwTP_region_outerldnTP_region_scotlandTP_region_southeTP_region_southwTP_region_walesTP_region_westmidTP_region_yorkshireTP_type_bikeTP_type_cyclistTP_type_driverTP_type_insd_pass_backTP_type_nkTP_type_otherTP_type_pass_backTP_type_pass_frontTP_type_pedestrianVechile_registration_presentVehicle_mobileWeather_conditions
Incident_details_present1.0000.0970.0500.1680.1200.2140.0020.0470.0200.0140.0290.0000.0190.0030.0070.0190.0000.0000.0280.0000.0000.0140.0040.0000.0000.2470.0270.2430.0160.0000.0000.0000.0000.0700.060
Injury_details_present0.0971.0000.1710.0260.0930.0190.0390.1430.1880.1490.1340.0500.0590.0000.0460.0430.0370.0620.0360.0350.0310.0220.0560.0000.0000.0870.1430.0610.0610.0280.0870.0000.0000.3750.080
Location_of_incident0.0500.1711.0000.1250.1550.0430.0300.0290.0410.0500.0310.0000.0270.0370.0000.0420.0100.0000.0750.0370.0370.0000.0130.0000.0360.0430.0220.0280.0390.0120.0460.0000.0180.3460.252
Main_driver0.1680.0260.1251.0000.2100.5100.0000.1040.0560.0500.0260.0220.0000.0120.0300.0000.0110.0000.0470.0630.0140.0350.0210.0190.0120.3960.0220.3760.0570.0000.0200.0000.0200.1790.130
Notifier0.1200.0930.1550.2101.0000.1270.0000.0380.0090.0410.0310.0080.0410.0000.0000.0210.0000.0000.0380.0170.0340.0330.0200.0000.0000.0900.0000.0760.0300.0420.0310.0000.0150.3070.240
PH_considered_TP_at_fault0.2140.0190.0430.5100.1271.0000.0000.0850.0640.0540.0470.0000.0000.0000.0000.0000.0000.0000.0390.0480.0010.0000.0170.0000.0000.4030.0300.3840.0460.0000.0370.0000.0000.0930.050
TP_injury_fatality0.0020.0390.0300.0000.0000.0001.0000.0690.0920.0280.0130.5210.0000.0000.0000.0980.0000.0000.2210.0350.0000.0280.0210.0050.0000.0320.1270.0010.0840.0000.0530.0000.0000.0380.044
TP_injury_nk0.0470.1430.0290.1040.0380.0850.0691.0000.0880.4210.1580.0560.0610.0450.0460.0600.0540.0390.1110.1170.0820.0830.0840.0100.0000.2100.2180.4540.1250.1160.1100.0000.0480.0940.023
TP_injury_traumatic0.0200.1880.0410.0560.0090.0640.0920.0881.0000.1570.0980.0750.1180.0220.2510.1880.0460.0640.2870.0580.0720.0660.1020.0320.0000.1050.1410.0730.0670.1270.2330.1210.0000.1060.000
TP_injury_unclear0.0140.1490.0500.0500.0410.0540.0280.4210.1571.0000.2320.0400.0560.1060.0380.1740.0340.0240.0670.3240.2920.0560.0610.0490.0170.3450.2450.4650.3550.0860.1850.0340.2900.1480.039
TP_injury_whiplash0.0290.1340.0310.0260.0310.0470.0130.1580.0980.2321.0000.0530.1270.2740.1090.2120.1250.1560.1510.0900.1490.3380.1850.0520.0210.1430.1490.1310.1260.4900.4470.0000.0770.1290.023
TP_region_eastang0.0000.0500.0000.0220.0080.0000.5210.0560.0750.0400.0531.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0900.0220.0320.0560.1160.0000.0000.0390.026
TP_region_eastmid0.0190.0590.0270.0000.0410.0000.0000.0610.1180.0560.1270.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1470.0380.0410.0130.0620.1690.1370.0000.0000.0360.000
TP_region_london0.0030.0000.0370.0120.0000.0000.0000.0450.0220.1060.2740.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2070.0000.1170.4180.0630.0000.0000.0530.015
TP_region_north0.0070.0460.0000.0300.0000.0000.0000.0460.2510.0380.1090.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0440.0000.0000.0930.0770.0000.0000.0390.000
TP_region_northw0.0190.0430.0420.0000.0210.0000.0980.0600.1880.1740.2120.0000.0000.0000.0001.0000.0190.0000.0000.0000.0000.0000.0030.0550.0000.0450.1390.1050.4150.0900.2180.0000.0000.0180.000
TP_region_outerldn0.0000.0370.0100.0110.0000.0000.0000.0540.0460.0340.1250.0000.0000.0000.0000.0191.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0140.0610.1430.1310.0000.0000.0000.000
TP_region_scotland0.0000.0620.0000.0000.0000.0000.0000.0390.0640.0240.1560.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0540.0000.0420.1090.0880.0000.0000.0320.033
TP_region_southe0.0280.0360.0750.0470.0380.0390.2210.1110.2870.0670.1510.0000.0000.0000.0000.0000.0000.0001.0000.0230.0000.0030.0130.0000.0000.1100.1930.0570.0500.2480.1530.0000.0350.0620.041
TP_region_southw0.0000.0350.0370.0630.0170.0480.0350.1170.0580.3240.0900.0000.0000.0000.0000.0000.0000.0000.0231.0000.0000.0270.0110.0000.0000.0340.0180.0570.2480.1100.1270.0000.0000.0310.031
TP_region_wales0.0000.0310.0370.0140.0340.0010.0000.0820.0720.2920.1490.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0320.0930.0100.1740.1230.1990.0000.4080.0600.030
TP_region_westmid0.0140.0220.0000.0350.0330.0000.0280.0830.0660.0560.3380.0000.0000.0000.0000.0000.0000.0000.0030.0270.0001.0000.0000.0620.0000.0460.0290.0420.1340.3220.1690.2160.0000.0300.000
TP_region_yorkshire0.0040.0560.0130.0210.0200.0170.0210.0840.1020.0610.1850.0000.0000.0000.0000.0030.0000.0000.0130.0110.0000.0001.0000.0000.0000.0420.0960.0520.0590.1330.1640.0000.0000.0360.011
TP_type_bike0.0000.0000.0000.0190.0000.0000.0050.0100.0320.0490.0520.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0620.0001.0000.0000.0620.0000.0330.0000.0000.0000.0000.0000.0000.000
TP_type_cyclist0.0000.0000.0360.0120.0000.0000.0000.0000.0000.0170.0210.0000.1470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.000
TP_type_driver0.2470.0870.0430.3960.0900.4030.0320.2100.1050.3450.1430.0240.0380.0000.0240.0450.0470.0000.1100.0340.0320.0460.0420.0620.0001.0000.0250.3770.0490.0540.1190.0710.0840.0860.036
TP_type_insd_pass_back0.0270.1430.0220.0220.0000.0300.1270.2180.1410.2450.1490.0900.0410.2070.0440.1390.0000.0540.1930.0180.0930.0290.0960.0000.0000.0251.0000.0310.0000.0310.0390.0000.0000.0610.000
TP_type_nk0.2430.0610.0280.3760.0760.3840.0010.4540.0730.4650.1310.0220.0130.0000.0000.1050.0140.0000.0570.0570.0100.0420.0520.0330.0000.3770.0311.0000.0680.0410.0940.0000.0000.0690.015
TP_type_other0.0160.0610.0390.0570.0300.0460.0840.1250.0670.3550.1260.0320.0620.1170.0000.4150.0610.0420.0500.2480.1740.1340.0590.0000.0000.0490.0000.0681.0000.0450.0690.0000.1640.0790.041
TP_type_pass_back0.0000.0280.0120.0000.0420.0000.0000.1160.1270.0860.4900.0560.1690.4180.0930.0900.1430.1090.2480.1100.1230.3220.1330.0000.0000.0540.0310.0410.0451.0000.2540.0000.0060.0730.037
TP_type_pass_front0.0000.0870.0460.0200.0310.0370.0530.1100.2330.1850.4470.1160.1370.0630.0770.2180.1310.0880.1530.1270.1990.1690.1640.0000.0120.1190.0390.0940.0690.2541.0000.0000.0000.0720.018
TP_type_pedestrian0.0000.0000.0000.0000.0000.0000.0000.0000.1210.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2160.0000.0000.0000.0710.0000.0000.0000.0000.0001.0000.0000.0000.000
Vechile_registration_present0.0000.0000.0180.0200.0150.0000.0000.0480.0000.2900.0770.0000.0000.0000.0000.0000.0000.0000.0350.0000.4080.0000.0000.0000.0000.0840.0000.0000.1640.0060.0000.0001.0000.0070.033
Vehicle_mobile0.0700.3750.3460.1790.3070.0930.0380.0940.1060.1480.1290.0390.0360.0530.0390.0180.0000.0320.0620.0310.0600.0300.0360.0000.0000.0860.0610.0690.0790.0730.0720.0000.0071.0000.397
Weather_conditions0.0600.0800.2520.1300.2400.0500.0440.0230.0000.0390.0230.0260.0000.0150.0000.0000.0000.0330.0410.0310.0300.0000.0110.0000.0000.0360.0000.0150.0410.0370.0180.0000.0330.3971.000
2025-07-04T15:03:46.188735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Claim NumberInception_to_lossIncident_details_presentInjury_details_presentLocation_of_incidentMain_driverNotification_periodNotifierPH_considered_TP_at_faultTP_injury_fatalityTP_injury_nkTP_injury_traumaticTP_injury_unclearTP_injury_whiplashTP_region_eastangTP_region_eastmidTP_region_londonTP_region_northTP_region_northwTP_region_outerldnTP_region_scotlandTP_region_southeTP_region_southwTP_region_walesTP_region_westmidTP_region_yorkshireTP_type_bikeTP_type_cyclistTP_type_driverTP_type_insd_pass_backTP_type_nkTP_type_otherTP_type_pass_backTP_type_pass_frontTP_type_pedestrianTime_hourVechile_registration_presentVehicle_mobileWeather_conditions
Claim Number1.0000.0290.3000.0650.0550.545-0.0580.4030.5550.0270.0710.0560.0500.0450.0000.0000.0090.0090.0000.0000.0000.0400.0440.0170.0000.0200.0000.0000.3370.0400.3250.0460.0190.0440.000-0.0030.0150.1220.147
Inception_to_loss0.0291.0000.0200.0000.0000.000-0.0110.0000.0000.0000.0000.0240.0000.0090.0000.0000.0030.0000.0000.0120.0000.0050.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0050.0000.007-0.0080.0000.0000.004
Incident_details_present0.3000.0201.0000.0970.0500.1680.0440.1200.2140.0020.0470.0200.0140.0290.0000.0190.0030.0070.0190.0000.0000.0280.0000.0000.0140.0040.0000.0000.2470.0270.2430.0160.0000.0000.0000.0390.0000.0700.060
Injury_details_present0.0650.0000.0971.0000.1710.0260.0510.0930.0190.0390.1430.1880.1490.1340.0500.0590.0000.0460.0430.0370.0620.0360.0350.0310.0220.0560.0000.0000.0870.1430.0610.0610.0280.0870.0000.1360.0000.3750.080
Location_of_incident0.0550.0000.0500.1711.0000.1250.0920.1550.0430.0300.0290.0410.0500.0310.0000.0270.0370.0000.0420.0100.0000.0750.0370.0370.0000.0130.0000.0360.0430.0220.0280.0390.0120.0460.0000.1520.0180.3460.252
Main_driver0.5450.0000.1680.0260.1251.0000.0570.2100.5100.0000.1040.0560.0500.0260.0220.0000.0120.0300.0000.0110.0000.0470.0630.0140.0350.0210.0190.0120.3960.0220.3760.0570.0000.0200.0000.1200.0200.1790.130
Notification_period-0.058-0.0110.0440.0510.0920.0571.0000.1000.0120.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0380.0000.0000.0000.0000.0000.0000.0000.1840.0000.2180.133
Notifier0.4030.0000.1200.0930.1550.2100.1001.0000.1270.0000.0380.0090.0410.0310.0080.0410.0000.0000.0210.0000.0000.0380.0170.0340.0330.0200.0000.0000.0900.0000.0760.0300.0420.0310.0000.1420.0150.3070.240
PH_considered_TP_at_fault0.5550.0000.2140.0190.0430.5100.0120.1271.0000.0000.0850.0640.0540.0470.0000.0000.0000.0000.0000.0000.0000.0390.0480.0010.0000.0170.0000.0000.4030.0300.3840.0460.0000.0370.0000.0000.0000.0930.050
TP_injury_fatality0.0270.0000.0020.0390.0300.0000.0000.0000.0001.0000.0690.0920.0280.0130.5210.0000.0000.0000.0980.0000.0000.2210.0350.0000.0280.0210.0050.0000.0320.1270.0010.0840.0000.0530.0000.0180.0000.0380.044
TP_injury_nk0.0710.0000.0470.1430.0290.1040.0000.0380.0850.0691.0000.0880.4210.1580.0560.0610.0450.0460.0600.0540.0390.1110.1170.0820.0830.0840.0100.0000.2100.2180.4540.1250.1160.1100.0000.0260.0480.0940.023
TP_injury_traumatic0.0560.0240.0200.1880.0410.0560.0000.0090.0640.0920.0881.0000.1570.0980.0750.1180.0220.2510.1880.0460.0640.2870.0580.0720.0660.1020.0320.0000.1050.1410.0730.0670.1270.2330.1210.0580.0000.1060.000
TP_injury_unclear0.0500.0000.0140.1490.0500.0500.0000.0410.0540.0280.4210.1571.0000.2320.0400.0560.1060.0380.1740.0340.0240.0670.3240.2920.0560.0610.0490.0170.3450.2450.4650.3550.0860.1850.0340.0400.2900.1480.039
TP_injury_whiplash0.0450.0090.0290.1340.0310.0260.0000.0310.0470.0130.1580.0980.2321.0000.0530.1270.2740.1090.2120.1250.1560.1510.0900.1490.3380.1850.0520.0210.1430.1490.1310.1260.4900.4470.0000.0350.0770.1290.023
TP_region_eastang0.0000.0000.0000.0500.0000.0220.0000.0080.0000.5210.0560.0750.0400.0531.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0900.0220.0320.0560.1160.0000.0200.0000.0390.026
TP_region_eastmid0.0000.0000.0190.0590.0270.0000.0000.0410.0000.0000.0610.1180.0560.1270.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1470.0380.0410.0130.0620.1690.1370.0000.0200.0000.0360.000
TP_region_london0.0090.0030.0030.0000.0370.0120.0450.0000.0000.0000.0450.0220.1060.2740.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2070.0000.1170.4180.0630.0000.0750.0000.0530.015
TP_region_north0.0090.0000.0070.0460.0000.0300.0000.0000.0000.0000.0460.2510.0380.1090.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0440.0000.0000.0930.0770.0000.0460.0000.0390.000
TP_region_northw0.0000.0000.0190.0430.0420.0000.0000.0210.0000.0980.0600.1880.1740.2120.0000.0000.0000.0001.0000.0190.0000.0000.0000.0000.0000.0030.0550.0000.0450.1390.1050.4150.0900.2180.0000.0140.0000.0180.000
TP_region_outerldn0.0000.0120.0000.0370.0100.0110.0000.0000.0000.0000.0540.0460.0340.1250.0000.0000.0000.0000.0191.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0140.0610.1430.1310.0000.0220.0000.0000.000
TP_region_scotland0.0000.0000.0000.0620.0000.0000.0000.0000.0000.0000.0390.0640.0240.1560.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0540.0000.0420.1090.0880.0000.0000.0000.0320.033
TP_region_southe0.0400.0050.0280.0360.0750.0470.0000.0380.0390.2210.1110.2870.0670.1510.0000.0000.0000.0000.0000.0000.0001.0000.0230.0000.0030.0130.0000.0000.1100.1930.0570.0500.2480.1530.0000.0280.0350.0620.041
TP_region_southw0.0440.0000.0000.0350.0370.0630.0000.0170.0480.0350.1170.0580.3240.0900.0000.0000.0000.0000.0000.0000.0000.0231.0000.0000.0270.0110.0000.0000.0340.0180.0570.2480.1100.1270.0000.0250.0000.0310.031
TP_region_wales0.0170.0000.0000.0310.0370.0140.0000.0340.0010.0000.0820.0720.2920.1490.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0320.0930.0100.1740.1230.1990.0000.0150.4080.0600.030
TP_region_westmid0.0000.0170.0140.0220.0000.0350.0000.0330.0000.0280.0830.0660.0560.3380.0000.0000.0000.0000.0000.0000.0000.0030.0270.0001.0000.0000.0620.0000.0460.0290.0420.1340.3220.1690.2160.0170.0000.0300.000
TP_region_yorkshire0.0200.0000.0040.0560.0130.0210.0000.0200.0170.0210.0840.1020.0610.1850.0000.0000.0000.0000.0030.0000.0000.0130.0110.0000.0001.0000.0000.0000.0420.0960.0520.0590.1330.1640.0000.0000.0000.0360.011
TP_type_bike0.0000.0000.0000.0000.0000.0190.0340.0000.0000.0050.0100.0320.0490.0520.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0620.0001.0000.0000.0620.0000.0330.0000.0000.0000.0000.0000.0000.0000.000
TP_type_cyclist0.0000.0000.0000.0000.0360.0120.0380.0000.0000.0000.0000.0000.0170.0210.0000.1470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.000
TP_type_driver0.3370.0000.2470.0870.0430.3960.0000.0900.4030.0320.2100.1050.3450.1430.0240.0380.0000.0240.0450.0470.0000.1100.0340.0320.0460.0420.0620.0001.0000.0250.3770.0490.0540.1190.0710.0230.0840.0860.036
TP_type_insd_pass_back0.0400.0000.0270.1430.0220.0220.0000.0000.0300.1270.2180.1410.2450.1490.0900.0410.2070.0440.1390.0000.0540.1930.0180.0930.0290.0960.0000.0000.0251.0000.0310.0000.0310.0390.0000.0310.0000.0610.000
TP_type_nk0.3250.0000.2430.0610.0280.3760.0000.0760.3840.0010.4540.0730.4650.1310.0220.0130.0000.0000.1050.0140.0000.0570.0570.0100.0420.0520.0330.0000.3770.0311.0000.0680.0410.0940.0000.0120.0000.0690.015
TP_type_other0.0460.0000.0160.0610.0390.0570.0000.0300.0460.0840.1250.0670.3550.1260.0320.0620.1170.0000.4150.0610.0420.0500.2480.1740.1340.0590.0000.0000.0490.0000.0681.0000.0450.0690.0000.0320.1640.0790.041
TP_type_pass_back0.0190.0050.0000.0280.0120.0000.0000.0420.0000.0000.1160.1270.0860.4900.0560.1690.4180.0930.0900.1430.1090.2480.1100.1230.3220.1330.0000.0000.0540.0310.0410.0451.0000.2540.0000.0140.0060.0730.037
TP_type_pass_front0.0440.0000.0000.0870.0460.0200.0000.0310.0370.0530.1100.2330.1850.4470.1160.1370.0630.0770.2180.1310.0880.1530.1270.1990.1690.1640.0000.0120.1190.0390.0940.0690.2541.0000.0000.0520.0000.0720.018
TP_type_pedestrian0.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.1210.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2160.0000.0000.0000.0710.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
Time_hour-0.003-0.0080.0390.1360.1520.1200.1840.1420.0000.0180.0260.0580.0400.0350.0200.0200.0750.0460.0140.0220.0000.0280.0250.0150.0170.0000.0000.0000.0230.0310.0120.0320.0140.0520.0001.0000.0000.3740.286
Vechile_registration_present0.0150.0000.0000.0000.0180.0200.0000.0150.0000.0000.0480.0000.2900.0770.0000.0000.0000.0000.0000.0000.0000.0350.0000.4080.0000.0000.0000.0000.0840.0000.0000.1640.0060.0000.0000.0001.0000.0070.033
Vehicle_mobile0.1220.0000.0700.3750.3460.1790.2180.3070.0930.0380.0940.1060.1480.1290.0390.0360.0530.0390.0180.0000.0320.0620.0310.0600.0300.0360.0000.0000.0860.0610.0690.0790.0730.0720.0000.3740.0071.0000.397
Weather_conditions0.1470.0040.0600.0800.2520.1300.1330.2400.0500.0440.0230.0000.0390.0230.0260.0000.0150.0000.0000.0000.0330.0410.0310.0300.0000.0110.0000.0000.0360.0000.0150.0410.0370.0180.0000.2860.0330.3971.000

Missing values

2025-07-04T15:03:40.485980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-04T15:03:40.702803image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Claim Numberdate_of_lossNotifierLoss_codeLoss_descriptionNotification_periodInception_to_lossLocation_of_incidentWeather_conditionsVehicle_mobileTime_hourMain_driverPH_considered_TP_at_faultVechile_registration_presentIncident_details_presentInjury_details_presentTP_type_insd_pass_backTP_type_insd_pass_frontTP_type_driverTP_type_pass_backTP_type_pass_frontTP_type_bikeTP_type_cyclistTP_type_pass_multiTP_type_pedestrianTP_type_otherTP_type_nkTP_injury_whiplashTP_injury_traumaticTP_injury_fatalityTP_injury_unclearTP_injury_nkTP_region_eastangTP_region_eastmidTP_region_londonTP_region_northTP_region_northwTP_region_outerldnTP_region_scotlandTP_region_southeTP_region_southwTP_region_walesTP_region_westmidTP_region_yorkshireIncurredCapped IncurredUnnamed: 46
012003-04-15PHLD003Head on collision2213Main RoadNORMALY10Othern/k\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t-\t-\t-\t1\t-\t-\t-\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-£\t-£\t-NaN
122003-04-20CNFLD003Head on collision19Main RoadWETY18Othern/k\t1\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t-\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-\t1\t-\t-\t-\t-£\t2,801£\t2,801NaN
232003-04-24CNFLD003Head on collision517Main RoadWETY16Yn/k\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t-\t-\t-\t1\t-\t-\t-\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-£\t1,221£\t1,221NaN
342003-05-13CNFLD003Head on collision123Main RoadN/KY14Othern/k\t1\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t-\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t-£\t3,530£\t3,530NaN
452003-06-11CNFLD003Head on collision148OtherN/KN9Othern/k\t1\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t-\t-\t-\t1\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-£\t3,156£\t3,156NaN
562003-06-24PHLD003Head on collision1623OtherN/KN0Othern/k\t1\t1\t-\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t1\t2\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-£\t10,502£\t10,502NaN
672003-07-16OtherLD003Head on collision54Main RoadNORMALN18Othern/k\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t-\t-\t-\t1\t-\t-\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-£\t91£\t91NaN
782003-07-17OtherLD003Head on collision040Main RoadWETY7Othern/k\t1\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t-\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t-\t-£\t9,130£\t9,130NaN
892003-07-20PHLD003Head on collision426Minor RoadWETY22Othern/k\t1\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t-\t-\t-\t1\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-£\t81£\t81NaN
9102003-07-29CNFLD003Head on collision285Main RoadN/KN22Othern/k\t1\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t-\t-\t-\t1\t-\t-\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-£\t447£\t447NaN
Claim Numberdate_of_lossNotifierLoss_codeLoss_descriptionNotification_periodInception_to_lossLocation_of_incidentWeather_conditionsVehicle_mobileTime_hourMain_driverPH_considered_TP_at_faultVechile_registration_presentIncident_details_presentInjury_details_presentTP_type_insd_pass_backTP_type_insd_pass_frontTP_type_driverTP_type_pass_backTP_type_pass_frontTP_type_bikeTP_type_cyclistTP_type_pass_multiTP_type_pedestrianTP_type_otherTP_type_nkTP_injury_whiplashTP_injury_traumaticTP_injury_fatalityTP_injury_unclearTP_injury_nkTP_region_eastangTP_region_eastmidTP_region_londonTP_region_northTP_region_northwTP_region_outerldnTP_region_scotlandTP_region_southeTP_region_southwTP_region_walesTP_region_westmidTP_region_yorkshireIncurredCapped IncurredUnnamed: 46
768176822015-06-29OtherLD003Head on collision134Main RoadNORMALN4YN\t1\t1\t1\t-\t-\t1\t1\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t2\t3\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-£\t930£\t930NaN
768276832015-06-29OtherLD003Head on collision1249Minor RoadNaNN10OtherN\t1\t1\t1\t-\t-\t2\t-\t1\t-\t-\t-\t-\t-\t-\t1\t-\t1\t1\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t2\t-£\t113,759£\t50,000NaN
768376842015-06-29OtherLD003Head on collision041Main RoadNaNY16Y#\t1\t1\t-\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-£\t-£\t-NaN
768476852015-06-29OtherLD003Head on collision073Main RoadNaNN16YN\t1\t1\t-\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-£\t6,085£\t6,085NaN
768576862015-06-30OtherLD003Head on collision1128Main RoadN/KN18YN\t1\t-\t1\t-\t-\t1\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t2\t-\t-\t-\t-\t-\t-\t-\t2\t-\t-\t-\t-\t-£\t17,503£\t17,503NaN
768676872015-06-30OtherLD003Head on collision183Main RoadNORMALN16OtherN\t1\t1\t1\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t-\t-\t-\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-£\t703£\t703NaN
768776882015-06-30OtherLD003Head on collision025Minor RoadNaNY14YN\t1\t1\t1\t-\t-\t1\t-\t1\t-\t-\t-\t-\t-\t-\t2\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t2£\t42,981£\t42,981NaN
768876892015-06-30OtherLD003Head on collision060Minor RoadNORMALY9OtherN\t1\t1\t-\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-\t1\t-\t-\t-\t-£\t5,175£\t5,175NaN
768976902015-06-30OtherLD003Head on collision1253Minor RoadNORMALN19OtherN\t1\t1\t1\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-£\t30,072£\t30,072NaN
769076912015-06-30OtherLD003Head on collision0266Minor RoadNORMALY14YN\t1\t1\t-\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t-\t1£\t1,925£\t1,925NaN